Digital cognitive training for functionality in mild cognitive impairment: a randomized controlled clinical trial
Bibliographic record
Abstract
Digital cognitive training (DCT) may improve cognition in people with mild cognitive impairment (MCI); however, its effect on functionality remains poorly defined. Despite preserved independence in Instrumental Activities of Daily Living (IADL), individuals with MCI perform them with increased effort. Conventional IADL measures may miss specific difficulties and crucial data. In contrast, the Canadian Occupational Performance Measure (COPM) is an individualized tool designed to identify challenges in IADLs. This study used the COPM to investigate the effects of DCT on functionality in individuals with MCI. Participants aged 60 or older with MCI were recruited for a double-blinded, randomized clinical trial comparing DCT to an active control group using commercial computer games. Results: Fifty-five participants were evaluated before and after 10 hours of intervention. The DCT group showed significant improvement in functionality, measured by COPM performance (pre: 5.66 [SD=2.29]; post: 6.87 [SD=1.72]), compared to the control group (pre: 5.64 [SD=1.64]; post: 5.72 [SD=2.23]). A main effect of time (F(1, 53)=7.07, p=0.01) and a group-time interaction (F(1, 53)=4.35, p=0.04) were observed. Additionally, learning over trials improved in the DCT group and decreased in controls. Significant effects of time (F(1, 53)=12.41, p<0.001) and group-time interaction (F(1, 53)=7.05, p=0.01) were observed. DCT improved functionality and learning in MCI, emphasizing the need for sensitive tools like the COPM to assess changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".